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Merged
sfc-gh-truwase merged 4 commits into from
Jan 5, 2026
Merged

Removed amp testcases #7745

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c749d78
[AMD] Skipped amp testcases for ROCm
k-artem Dec 23, 2025
80f43a7
Merge branch 'master' into skipped_amp_testcase
sfc-gh-truwase Dec 24, 2025
a7eac8c
Remove whole TestAmp from half_precision/test_fp16.py
k-artem Jan 5, 2026
56a2ec0
Merge branch 'master' into skipped_amp_testcase
k-artem Jan 5, 2026
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112 changes: 0 additions & 112 deletions tests/unit/runtime/half_precision/test_fp16.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,13 +15,6 @@
from deepspeed.ops.op_builder import CPUAdamBuilder, FusedLambBuilder
from deepspeed.moe.utils import split_params_into_different_moe_groups_for_optimizer

try:
from apex import amp # noqa: F401 # type: ignore
_amp_available = True
except ImportError:
_amp_available = False
amp_available = pytest.mark.skipif(not _amp_available, reason="apex/amp is not installed")

if torch.half not in get_accelerator().supported_dtypes():
pytest.skip(f"fp16 not supported, valid dtype: {get_accelerator().supported_dtypes()}", allow_module_level=True)

Expand Down Expand Up @@ -534,111 +527,6 @@ def test(self, zero_stage, use_cpu_offload):
model.destroy()


@amp_available
class TestAmp(DistributedTest):
world_size = 2

def test_adam_basic(self):
if not get_accelerator().is_fp16_supported():
pytest.skip("fp16 is not supported")
config_dict = {"train_batch_size": 2, "steps_per_print": 1, "amp": {"enabled": True}}
hidden_dim = 10

model = SimpleModel(hidden_dim)
optimizer = torch.optim.Adam(params=model.parameters())
model, _, _, _ = deepspeed.initialize(config=config_dict, model=model, optimizer=optimizer)
data_loader = random_dataloader(model=model, total_samples=50, hidden_dim=hidden_dim, device=model.device)
for n, batch in enumerate(data_loader):
loss = model(batch[0], batch[1])
model.backward(loss)
model.step()

@pytest.mark.skipif(not deepspeed.ops.__compatible_ops__[FusedLambBuilder.NAME],
reason="FusedLambBuilder has not been implemented on this system")
def test_lamb_basic(self):
if not get_accelerator().is_fp16_supported():
pytest.skip("fp16 is not supported")
config_dict = {
"train_batch_size": 2,
"steps_per_print": 1,
"optimizer": {
"type": "Lamb",
"params": {
"lr": 0.00015
}
},
"gradient_clipping": 1.0,
"amp": {
"enabled": True,
}
}
hidden_dim = 10

model = SimpleModel(hidden_dim)
model, _, _, _ = deepspeed.initialize(config=config_dict, model=model, model_parameters=model.parameters())
data_loader = random_dataloader(model=model, total_samples=50, hidden_dim=hidden_dim, device=model.device)
for n, batch in enumerate(data_loader):
loss = model(batch[0], batch[1])
model.backward(loss)
model.step()

def test_adam_O2(self):
if not get_accelerator().is_fp16_supported():
pytest.skip("fp16 is not supported")
config_dict = {
"train_batch_size": 2,
"steps_per_print": 1,
"optimizer": {
"type": "Adam",
"params": {
"lr": 0.00015
}
},
"gradient_clipping": 1.0,
"amp": {
"enabled": True,
"opt_level": "O2"
}
}
hidden_dim = 10

model = SimpleModel(hidden_dim)
model, _, _, _ = deepspeed.initialize(config=config_dict, model=model, model_parameters=model.parameters())
data_loader = random_dataloader(model=model, total_samples=50, hidden_dim=hidden_dim, device=model.device)
for n, batch in enumerate(data_loader):
loss = model(batch[0], batch[1])
model.backward(loss)
model.step()

def test_adam_O2_empty_grad(self):
if not get_accelerator().is_fp16_supported():
pytest.skip("fp16 is not supported")
config_dict = {
"train_batch_size": 2,
"steps_per_print": 1,
"optimizer": {
"type": "Adam",
"params": {
"lr": 0.00015
}
},
"gradient_clipping": 1.0,
"amp": {
"enabled": True,
"opt_level": "O2"
}
}
hidden_dim = 10

model = SimpleModel(hidden_dim)
model, _, _, _ = deepspeed.initialize(config=config_dict, model=model, model_parameters=model.parameters())
data_loader = random_dataloader(model=model, total_samples=50, hidden_dim=hidden_dim, device=model.device)
for n, batch in enumerate(data_loader):
loss = model(batch[0], batch[1])
model.backward(loss)
model.step()


@pytest.mark.parametrize("zero_stage", [1, 2, 3])
@pytest.mark.parametrize("optimizer_constructor", [FusedAdam, torch.optim.Adam])
class TestZeroSupportedClientOptimizer(DistributedTest):
Expand Down